from __future__ import print_function
import tensorflow as tf
from sklearn.datasets import  load_digits
from sklearn.cross_validation import  train_test_split
from sklearn.preprocessing import LabelBinarizer

digits = load_digits()
X = digits.data
y = digits.target
y = LabelBinarizer().fit_transform(y)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.3)

def add_layer(inputs, in_size, out_size, layer_name, activation_function=None, ):
    Weights = tf.Variable(tf.random_normal([in_size, out_size]))
    biases = tf.Variable(tf.zeros([1, out_size]) + 0.1)
    Wx_plus_b = tf.matmul(inputs, Weights) + biases
    if activation_function is None:
        outputs = Wx_plus_b
    else:
        outputs = activation_function(Wx_plus_b, )
    return outputs


keep_prob = tf.placeholder(tf.float32)
xs = tf.placeholder(tf.float32, [None, 64])
ys = tf.placeholder(tf.float32, [None, 10])

L1 = add_layer(xs, 64, 50, 'L1', activation_function=tf.nn.tanh)
prediction = add_layer(xs, 50, 10, 'L2', activation_function=tf.nn.softmax)

cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys * tf.log(prediction), reduction_indices=[1]))

train_step = tf.train.GradientDescentOptimizer(0.1).minimize(cross_entropy)

sess = tf.Session()
merged = tf.summary.merge_all()
sess.run(tf.global_variables_initializer())

for i in range(500):
    sess.run(train_step, feed_dict={xs: X_train, ys: y_train, keep_prob: 0.5})
    if i % 50 == 0:
        train_result = sess.run(merged, feed_dict={xs: X_train, ys: y_train, keep_prob:1})


